Gezgin satıcı problemlerinin çözümü için rassal anahtar temelli elektromanyetizma sezgiselinin uygulanması

Ağ optimizasyonu problemleri içerisinde, gezgin satıcı problemi literatürde yaygın bir şekilde çalışılan problemlerden biridir. Problemin hesaplama açısından zor olması dolayısıyla, optimal çözümü elverişli zamanda elde edebilmek için pek çok sezgisel algoritma geliştirilmiştir. Bu çalışma, simetrik gezgin satıcı problemlerinin çözümü için melez bir elektro-manyetizma sezgiseli sunmaktadır. Esasında, elektro-manyetizma sezgiseli, fizikteki elektromanyetizma teorisinden ilham alan, popülasyon tabanlı global bir arama algoritmasıdır. Önerilen mekanizma, fizibil alanda rassal olarak oluşturulan partikülleri optimal çözüme yaklaştırma prensibine dayanır. Bu çalışmada, araç rotalama problemlerini çözebilmek için rassal anahtar yaklaşımı elektromanyetizma sezgiseline adapte edilmiştir.  15 kıyaslama örneği üzerinde test edilen sezgisel yöntem küçük boyuttaki problemler için en iyi çözümleri üretmektedir. Ayrıca, ağdaki nokta sayısı arttıkça, önerilen algoritma optimale yakın çözümler üretmektedir. Sonuçların etkinliği, önerilen algoritmanın kombinatoryal optimizasyon problemleri çözümü için de değerlendirilebileceğini göstermektedir.

Application of the random key based electromagnetism-like heuristic for solving travelling salesman problems

Among network optimization problems, travelling salesman problem is widely studied one in the literature. Since the problem is computationally hard, many heuristics have been developed to obtain the optimal solution in reasonable time. This paper introduces a hybrid electromagnetism-like heuristics to solve symmetric travelling salesman problems. Originally, electromagnetism-like heuristic is a population based global search algorithm that is inspired by electromagnetism theory in Physics. The proposed mechanism is based on the principle of encouraging randomly generated particles to move toward to optimal solution in the feasible area. In this paper, random-key approach is adapted to electromagnetism-like heuristic to solve vehicle routing problems. Regarding 15 benchmark instances, proposed heuristic procedure produces optimal solutions for small instances. Moreover, as the number of vertices increase in the network, the proposed algorithm generates near optimal solutions. Efficiency of results shows that the proposed algorithm can be evaluated for the solution of combinatorial optimization problems.

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